State estimation using filtering methods applied for aircraft landing maneuver

State estimationmethods are the popular means of validating aerodynamic characteristics on maneuvering Aircraft. This work deals with adaptation of familiar filtering methods for Aircraft landing maneuvers, to estimate the aircraft touchdown states. The mathematical model for two-point landings (main wheel in contact with the ground and nosewheel airborne) consists of nonlinear flight mechanics equations representing Aircraft longitudinal dynamics. A nonlinear 6 DOF pilot in loop simulation model is used for the measurement of data generation that was mixed with process and measurement noises. These values are used for posterior state correction in the implementation of Kalman filter. With the state values just before the initiation of flare as initial conditions, filters such as Upper Diagonal factorized form of Adaptive Extended Kalman Filter (UDAEKF) and Unscented Kalman Filter (UKF) is implemented in Matlab environment. The estimated states and measured data are compared using performance metrics for vertical acceleration (Nz) which brings out the possibility of over quantification (3.5%) and under quantification (11.3%) at onset of touchdown having an impact on landing loads. As observed, the performance of UKF is two and half times faster than UDAEKF through superior state propagation.